One man's steps to a greener lifestyle.

For some time now my Home Energy Management System (HEMS) has been managing many of my domestic electricity consumers including:

car charging

dishwasher

home storage battery

washing machine

water heating via immersion heater

Domestic solar panels

The overarching strategy has been to:

maximise use of my own solar energy (rather than consume from the grid)

prioritise consumers for best value within the constraint of available solar generation

when power is needed from the grid to optimise the purchase price by shifting consumption to the cheapest periods (my price changes every half an hour)

For some consumers such as car charging and water heating this has resulted in those consumers switching between two modes:

self-consumption when they are enabled to use the ‘free’ electricity from my own solar panels (subject to device prioritisation) with some proportional control

boost when they run at full power drawing some if not all of the required power from the grid at the lowest available price

However the quite exceptional stress being put on the grid in the UK has prompted some expansion of capability. It’s normal once in a while that my electricity prices go negative, commonly caused by the overlap of large amounts of renewable electricity on the grid (e.g. excess solar output on sunny afternoons and/or high windfarm output due to wind conditions) and low demand (summer nights without heating demand, summer afternoons, bank holidays etc) which is exacerbated by the current corona situation. The current corona situation has made this more common and the plunge pricing events more extreme with multiple hours of negative pricing today some of which are into double digits (which I think is unprecedented). For car charging and water heating this has resulted in a new control mode.

Stepped Mode 3 EVSE in consumer unit case

Hot water cylinder with immersion heater

The new control mode is a disabled status where the the device neither self-consumes nor is forced on. In the short term this increases export to the grid, but the mode is intended to help balance the grid disabling consumption for now to enable more consumption when the grid is under most extreme stress from an excess of generation over demand. Or to think of it another way, it passes up the opportunity to use free electricity now in order to be paid to use electricity later, responding to the price incentive to support balancing the grid.

Thus on a normal day these devices switch half-hourly between self-consumption (free electricity) and Boost (paid for electricity), but on a price plunge day then they switch half hourly between the new disabled state (no electricity, increased export) and the existing Boost (but now paid-to-consume electricity).

The full availability of modes is thus:

mode / other

abbreviation

battery storage

car charger

water heating

Make and model

Powervault G200

N/A

Immersun v2

Control means

API

Relay

Mixed API and relay

Mode – Boost

++

Powervault “Force charge”

via HEMS relay (Ch 1)

Via HEMs relay (Ch 2) to Immersun “External Boost” input.

Mode – Self-consumption only

+

Priority #1Powervault “Charge only”

Priority #2via Immersun relay output (Ch 3)

Priority #3Immersun default behaviour

Mode -Disabled

0

N/A – Available in API but not used by HEMS.

via HEMS relay (Ch 4)

Immersun “Holiday” mode via API

Mode – Both self-consumption and self-discharge available

+/-

Powervault “Normal” via API

N/A – No reverse flow from car to home available (not V2X capable)

N/A

Major device modes available to HEMS

We should thus be better equipped to support the grid in the current circumstances.

Import / Export at the smart meter for Saturday 23rd May

The chart above shows the resulting behaviour. In particular the large negative currents through the morning to early afternoon show that much of the normal self-consumption has been disabled. Then from mid-afternoon the import shows the effect of enabling multiple consumers simultaneously. Here the behaviour of the car charging and water heating was boosted as this point, while the dishwasher’s and washing machine’s existing behaviour added to load.

Not that it has anything to do with the revised controls, but the spikiness of the export during the day shows the highly variable nature of the export through the day being a function of both variable generation through passing clouds and variable consumption with kettle boils and the like. Thus it’s important that consumers for self-consumption have automated closed loop control since manual control of an immersion heater or car charger to achieve high self-consumption with minimal import would have been almost impossible even with a level of human intervention wholly at odds with the scale of savings achieved – small savings hour-by-hour add up over the day, weeks and months but their value is relatively tiny compared to the labour to attempt equivalent control manually.

Here is a similar import-only half-hourly view from the smart meter WAN side:

Half-hourly consumption and cost from smart meter WAN side.

The screenshot above clearly shows those periods of import being targeted at the periods with the most negative prices. Since different consumers need power for different periods of time (for example it takes about 7 hours to charge the battery, but only 2 or 3 for a tank of hot water) then consumption rises as cost falls. My consumption-weighted average cost was -6.22 p/kWh yesterday. However the same price point during the day or night has delivered different consumption from the grid as the output of the solar panels must be consumed before consumption from the grid starts. We are still some way from the point where it becomes economically attractive to disable the solar panels.

Daily electricity cost for 28 days (vertical scale is fractions of a pound)

Finally the above image shows the last 28 days of electricity average cost in p/kWh. Although some other days included some periods of negative pricing, the quite exceptional pricing yesterday is amply illustrated with the combination of extreme prices and the new load management mode delivering revenue (i.e. negative cost) of 82.4 pence through consumption of 13.252 kWh at an average 6.22 p/kWh.

This is something of a zero sum game in terms of consumption as I don’t artificially increase consumption to improve income – such as leaving the oven on with the door open during a summer day – this is all about shifting consumption that would have happened anyway. However we have not only supported the grid when it is most stressed but also reduced our energy costs significantly (to the point of being significantly negative) by moving consumption from being predominately self-consumption (i.e. from our own solar panels) to being predominantly grid consumption.

For some time now I’ve been thinking about creating a real time display which pulls together data from a variety of sources around the home to provide an overview of what’s going on without the need to visit multiple web pages or apps. Until the last 10 days or so that involved little more than thoughts of how I might evolve the existing immersun web page with more content (I don’t have the skills to write my own app), but then about 10 days ago I saw an online gauge that someone else had created to show energy price and inspiration struck. Ten days later I have my monitor working, albeit not complete:

HEMS monitor

The monitor pulls together information from:

My electricity tariff for p/kWh

My immersun for power data (to/from: grid, solar, water, house)

My storage battery for power in/out and state of charge

My HEMS for electricity cost thresholds between different battery modes.

The gauge consists of two parts: (i) an upper electricity cost part and (ii) a lower power part.

The upper electricity cost part is effectively a big price gauge from 0 p/kWh to 25 p/kWh with a needle that moves each half hour as the price changes. It has various features:

The outer semi-circular ring (blue here) shows today’s relationship between battery mode and electricity price. Today is very sunny, so no electricity was bought from the grid to charge the battery, and this part is all blue for normal battery operation. If the days was duller and electricity was to be bought to charge the battery, then two further sectors would appear:

a dark green sector from zero upwards showing the grid prices at which the battery would be force charged from the grid, and

a light green sector showing when the battery is not permitted to discharge but may continue to charge from solar.

In inner semi-circular ring (green / yellow / red here) currently just colour-codes increasing electricity price, but will be used to show today’s prices at which car charging and water heating are triggered from the grid.

The current price per kWh is taken from Octopus’s price API, while the current cost per hour is derived both from this and the grid power from the immersun.

The needle grows from a simple dot indicating the price per kWh only when no power is drawn from the grid to a full needle when the electricity cost is 10 pence per hour or more.

The lower power part is effectively a power meter ranging from 5,000 Watts of export to the left to 5,000 Watts of import to the right. It updates every few seconds. It has various features:

The outer semi-circular ring (orange /maroon / green here) shows how power is being consumed:

orange – shows consumption by the house less specified loads

maroon – shows battery charging

blue (not shown) – shows water heating

green – shows export to the grid

The inner semi-circular ring (yellow here) shows the source of power. The sum of the sources should equal the sum of the consumers. The sources are:

maroon (not shown) – shows battery discharge

yellow – shows solar power

red (not shown) – shows grid power

The power value shows the net import or export from / to the grid, while SoC refers to the state of charge of the battery (0-100%). The combination of import power and electricity price gives the cost per hour in the top gauge.

The needle position shows net import (to the right) or next export (to the left). The needle should thus be to the left of the green sector, or to the right of the (unseen) red sector. Needle length show the full power being handled and is thus proportionate to the angle of the sector including all the colours in the lower gauge and extends from 0 to 5 kW.

Monitor installed on an old phone in the kitchen.

The gauge scales to fill the smallest of screen height or width and translates to be centrally positioned regardless of screen size. My intention is to display it on an old mobile phone as an energy monitor, but I can also access it on any web browser on any device within the home.

Much of the optimisation of my home exploits the cost-saving potential of the Octopus Agile electricity tariff. This tariff is a radical departure from a typical UK tariff. Rather than a fixed unit price that applies 24/7, the Agile tariff exploits the smart meter to provide up to 48 different half-hourly prices per day which change day-to-day according to a formula linked to daily electricity auctions in an absolutely transparent manner.

Octopus’ transparent pricing

The prices can vary from several pence per kWh negative (i.e. being paid to use electricity) to a cap at 35 p/kWh which might apply in the early evening. That therefore places a some risk on the consumer, but equally can provide significant benefit. There are regional differences in the equation reflecting variation in the costs to distribute electricity in different parts of the country, so your formula might not be exactly the same as mine, but will be very similar.

Agile bill summary for two months ending mid-April 2020.

That average 5.02 p/kWh is not just competitive versus other tariffs – it tramples all over them. Personally I’m continually plagued by advertising claiming to save me £100s versus existing tariffs, but none ever comes remotely close to this rate. Thus, with my consumption pattern, I think that this is an unrivalled tariff. The Energy Saving Trust has calculated the average UK electricity cost at 15.75 p/kWh as at March 2019 (their latest analysis at the time of writing), so I’m currently paying a third of that average rate.

Of course you won’t ever see this tariff recommended on a switching website. Indeed, I’ve never seen any smart tariff recommended on a switching website, because they only ever seem to offer the choice between flat rate tariffs and Economy 7. In my opinion the lack of smart tariffs always denies consumers access to what may genuinely be the cheapest tariff for them by short term switches between tariffs that are pretty much alike. Those switching sites don’t want consumers to find long-term value, instead they want consumers to keep switching so that they keep earning commission.

However for me, not only have Octopus demonstrated long-term value, but they provide 100% renewable power, and are the only electricity company to be Which? recommended for 3 consecutive years. For you there’s a further £50 to be saved if you click on the image below and sign-up for any one of Octopus’ tariffs.

As regular readers will recall I’ve recently updated my battery controls with solar predication. For some years now my battery storage has automatically charged during sunshine and later discharged in the evening, and over the last year I added the HEMS to manage buying electricity including for the battery when the electricity cost was cheapest as there’s little solar in the winter, but now the HEMS has the ability to automatically adjust how much power is bought from the grid depending on how much output is expected from the solar panels in order to deliver a charged battery by the end of the day when the electricity price rises significantly. The battery charging can thus swing from all solar to all grid and all points in between entirely automatically based on the solar forecast.

Predicted solar output for March 16th to 18th as of the evening of March 15th.

Now however I’ve also added the capability to adjust when the wet goods – dishwasher and washing machine – run as a function of expected solar panel output.

Siemens Dishwasher

Bosch Washing Machine

As with the battery controls, I don’t attempt to match generation half hour by half hour with device operation because of uncertainty around how precisely a solar forecast from the evening before will match actual solar production up to twelve hours later. Instead my solar algorithm extracts the number of hours of significant solar production and the earliest start time of that production. For the battery that information is used to modify the number of hours of battery charging to be bought from the grid and the end of the window during which those hours of charging may be bought. Now I’ve updated the controls for the wet goods similarly.

The existing wet good controls look to start the machine at the time when the electricity cost for the cycle is cheapest within a time window set by the user, so the user defines the earliest and latest acceptable start times and the algorithm finds the cheapest start time within that window. The updated wet goods controls assess the number of hours of solar charging available and if both (i) the window exceeds a certain size and (ii) the user’s time window includes the solar window then the user window is narrowed to start at the start of the solar window. The resulting start time between the start of the solar window and the end of the user window may not be absolutely the cheapest grid cost but my assumption is that the solar contribution (which could be up to 100%) will in practice make this the cheapest grid cost as any power needed from the grid will be at a relatively good price. If this start time in the solar window does not reflect absolutely the cheapest grid cost then a second start time may also be identified which is the cheapest grid cost.

HEMS schedule with start times for wet goods within the solar window.

In the above example the start times are both within the solar window and the cheapest energy price, so no cheaper alternative is also offered. Start times within the solar window tend to be in the afternoon as the grid energy costs are lower. This also improves the probability that the battery may briefly discharge if the total load exceeds the solar output. However in the above example the energy price is so low that the battery is also force-charging (it’s dark green) so any surplus demand will come from the grid.

HEMS schedule with start times both inside and outside the solar window

In the above example the cheapest time to run each cycle from the grid is at night, although given the availability of solar during the day then any small saving in grid costs at night is very likely wiped out by the ability to run some (if not all) of the cycle during the day from the solar surplus rather than the grid. Both start times are available – the absolutely lowest grid cost and the lowest grid cost during the solar window.

It’s probably also worth mentioning the implications of running the appliance alongside the battery status being different colours:

Blue battery and state of charge being at or over 80% – appliance is prioritised over battery allowing battery to discharge to meet appliance needs as required – least likely to draw anything from grid (but likely highest cost to draw from grid)

Light green battery or (blue battery and state of charge being below 80%) – appliance is prioritised over battery charging alone (so battery may not discharge to support appliance) – a little more likely to draw anything from grid (but likely mid cost to draw from grid)

Dark green battery – battery charging and appliance are equal priority – most likely to draw something from grid (but likely cheapest cost to draw from grid, even to the point of being paid to draw from grid at times)

Today we are increasingly reliant upon the internet. During the current crisis when many are confined to home the internet provides opportunities unimaged to previous generations. Whether it’s online shipping, entertainment, using zoom or other online meeting tools to maintain family, society or church connections; the internet provides the means to supply us with our material needs, entertainment and social contacts in ways unimagined previously.

However our home, and other smart homes, may be particularly vulnerable should the internet become unavailable either in general or a particular service upon which we rely becomes unavailable. I thus thought that for this post I’d reflect upon the services our home relies upon for normal operation and what the impact of their absence would be. I will also reflected upon other failures of the service where such failures have been previously observed.

HEMS schedule for 11-12/04/2020

Service

Effect if unreachable

effect of other observed failure

Solar forecasting

In unreachable when daily schedule being generated then schedule assumes no solar production like mid-winter.

On April 7th I observed several hours around the middle of the day when there were no forecasted or estimated actuals reported, but the service continued to accept our measured readings. No impact on HEMS operation.

Agile electricity price data.

If unreachable when daily schedule being generated then schedule carries over from prior. Schedule may be generated by manual initiation of the script when the required data becomes available.If necessary operation of the car charger PLC could be suspended causing the car to charge at full power immediately and the car’s own timer used to set operating hours if needed.

If tomorrow’s price data is not yet published (i.e. it’s overdue), then the optimisation is performed using the most recent day’s price data. Since today will no longer be within the dataset provided by the solar forecast (as it’s no longer in the future) then no solar window will be found and so all the required number of hours will be bought assuming today’s (not tomorrow’s) prices, so the battery will still be charged but at a sub-optimal cost. The optimisation can be started manually once the overdue pricing data is available.

Immersun API

If the real time generation data wasn’t available at all then solar car charging would be disabled. Car charging based on bought electricity price would continue.

I have observed occasions when the real time data ceases to be updated (presumably because communications between immersun and cloud is lost) which then throws out the upload to the solar forecasting and the disabling of solar car charging at low generation levels. The forecasting site doesn’t seem to be phased by some erroneous data having never yet dropped below 0.96 correlation. The solar car charging has once been enabled later than it should which had some impact on battery state of charge, but was disabled slightly later via the immersun relay output.

An API is also used to switch the immersun into or out of holiday mode on days with significant periods of negatively-priced electricity. Unavailability of the API could leave the immersun locked in holiday mode and thus completely unable to heat water, or not in holiday mode when it should be causing self-consumption to continue when export would be more optimal to allow for negatively-priced import later. Vacation mode may be enabled or disabled manually via the immersun’s front panel to mitigate.

Powervault API

Lack of availability of the API would disable the ability to switch operating modes. This would leave the Powervault either charging from solar only or force charging, and may or may not permit discharge. The unit can be reset to normal state via repeated operation of front panel button which would disable scheduling but provide default solar operation.

WIFIPLUG API

Lack of availability would prevent the wet goods being turned on via API. Operation via the bushputton on the plug and indeed via Apple Homekit should remain.

Apple Homekit

The heating and other automations are run from Apple Homekit. My understanding is that the local service is provided from the two Apple TVs acting as hubs which should continue. Remote access via devices not in the home would be disabled.

Hildebrand API

I have access to an API giving data from the HAN side of my smart meter and independent current measurement, but this is not currently used for control so no operational impact.

Impact assessment for loss of different cloud services

Overall I would conclude that there’s no significant issue here. The house would continue to be heated and appliances will still work. Any impact would be around energy consumption and cost only.

Last night rather unexpectedly my solcast solar irradiance data tuned itself. I use this data to predict the output of my solar panels and adjust what I buy from the grid in response. I had expected that tuning would happen eventually, but my understanding was that two month’s data was required, not the two week’s data that I had so far supplied.

Prior to tuning my predictions had looked like this..

solcast predictions before tuning

Although the system is clearly predicting output to a reasonable degree of accuracy, there are two obvious issues:

The orientation of the array from due south seems a little off, as my array starts and ends generation earlier than the prediction suggesting the the orientation should be slightly more easterly in the model.

The peak at a sustained 4 kW is overly optimistic. The panels can generate 4 kW according to my monitoring, but only relatively briefly and certainly not for more than an hour in March.

Nevertheless, the overall identification of better and worse days is clearly working.

solcast predictions after tuning

However after tuning both issues have been resolved. The measured and predicted curves match very closely. Note that the prior predictions have been updated by the tuning process, so March 26th which is included in both the images looks subtly different. Note also that my control is based on forecasts from the evening before, not the ‘1 hour ahead forecasts’ illustrated above. You may also observe that there is a discrepancy in the morning of April first, where the measured data is zero while the estimated actuals and forecasts are quite healthy, which arises as a result of a temporary failure of the immersun server from where the data is taken.

The ability to do tuning requires an upload of data. I upload generation data continuously at 5-minute intervals (the shortest allowed interval) which may explain the early availability of the tuned results. The script that I use to achieve this takes data indirectly from my ImmerSUN and is modified from this script. I achieved a 99% correlation which is pretty good. Subsequently it seems that the tuning takes place automatically each day.

As winter turned to spring my thoughts for my HEMS turned to thinking about how to adjust the operation of the HEMS in managing battery charging to account for anticipated solar production. Previously the HEMS was configured to buy a preset number of hours of charge from the grid each day, typically overnight when the power tends to be cheapest. Through 2019 as the seasons changed I periodically adjusted this figure to create headroom to store charge from the solar panels later in the day. However I would like to make this adjustment automatically day by day.

Late last year I came across solcast a website that predicts the output of solar panels. The user creates an account, describes their PV array (location, capacity and orientation), and can then download predictions via API.

Solar prediction for the next three days

The orange line shows the predicted output for the next few days, with the light grey area showing the confidence interval from 10 to 90%. As a prediction there’s a degree of uncertainty associated with the prediction as there is with a weather forecast. The 10% line shows that 1 day in 10 the output will be lower than the grey area, while the 90% line shows that 1 day in 10 then output will be higher than the grey area.

My original prediction were based on the orange line (the 50th percentile) where output was equally likely to be above or below this amount. However my risk on an incorrect prediction is not even. If I fail to buy enough power from the grid when the price is low then I risk paying up to 35p/kWh to buy when the price is high, whereas if I buy an unnecessary cheap kWh from the grid I may spend an unnecessary 5p/kWh on average. Thus I decided to take a more conservative position on risk to obtain the lowest cost position. I opted for a 20% risk, so 1 day in 5 I might underestimate my purchase from the grid reflecting the ratio of grid prices high:low. I estimated this 20th percentile assuming a normal distribution on the low side where the 20th percentile = 0.34 * 50th percentile + 0.66 * 10th percentile.

The process is as follows:

download the data in a half-hourly format to be comparable to the half-hourly Agile electricity price data,

calculate the 20th percentile from the 10th and 50th percentiles,

count the number of half-hours in the 20th percentile data above a threshold that provides for charging the battery at full power,

establish the earliest half hour when solar would charge the battery at full power,

adjust the period for buying power from the grid by the period anticipated for solar charging (tomorrow a total of 7 hours is to be achieved by 6.5 hours from solar leaving 0.5 hours from the grid),

adjust the end time for buying power from the grid to align with the earliest hour when the battery can charge at full power from solar.

The result of these calculations can be seen above. One half-hour of lowest price battery charging has been identified overnight to meet the requirement for charging from the grid. Normal HEMS behaviour has also identified several other periods of grid charging at lower cost during the day, but these are not counted towards the target for buying from the grid due to the potential for a double count of solar and grid charging during the day. (In a similar manner there are multiple start times during the afternoon when the dishwasher or washing machine can be run on grid power more cheaply than the optimal overnight start time.)

No explicit command from the HEMS is required to enable proportional charging of the battery from the solar panels when there is a solar surplus as 2 of the 3 used battery operating modes (normal and charge-only) have this capability, and the third mode force charges the battery. If the battery is force charged during solar surplus then the source of the energy will of course be the solar panels, but any shortfall will be met from the grid.

mode

charge behaviour

discharge behaviour

comment

Normal

Proportional to solar surplus

Proportional to shortfall

Used at high grid prices (today discharging enabled at > 8 p/kWh)

Charge only

Proportional to solar surplus

No discharge

Used at mid grid price to save stored energy for period of higher grid price (between 5 and 8 p/kWh today)

Force charge

Full power

No discharge

Used at lower grid price to buy from grid (today buying at < 5p/kWh). If low price occurs at time with reasonable solar generation then use of solar output will happen automatically, with only any shortfall coming from the grid.

Solar PV installations like mine that are a few years old generally qualify for the UK’s Feed-in Tariff (FiT) which pays both for generation and notionally for export, while newer installations are covered by the Smart Export Guarantee (SEG). The older FiT scheme was universal in the sense that all larger electricity companies had to participate and they all paid the same rates, while with the newer scheme there’s still an obligation for larger companies to participate but the rates are all different. Older installations like mine can optionally swap the export component of the FiT for the SEG, but is that an attractive option?

My FiT export payment is currently 5.38 p/kWh on a deemed export basis, which means that, rather than measure actual export, it is assumed that half of my generation is exported. My electricity supplier Octopus offerers one of the best SEG rates at 5.5 p/kWh but that’s on the actual export, not the deemed export.

Monitoring data March 2019 – February 2020

Alternative

Description

Energy exported

Rate paid

Total

Comment

Baseline FiT

2,098 kWh (50% of 4,196.1 kWh)

5.38 p/kWh

£112.87

Scenario #1

Switch to SEG without other changes

647.1 kWh

5.50 p/kWh

£35.59

68% reduction

Scenario #2

Add disable water heating from solar to above.

1,722.4 kWh (1,075.3 + 647.1 kWh)

5.50 p/kWh

£94.75

Provide equivalent water heating from gas

1,075.3 kWh

3.2 p/kWh / 90%

(£38.23)

Total

£56.52

50% reduction

Octopus Energy does also offer the alternative of a variable export rate based on wholesale prices, akin to their Octopus Agile import tariff, but for export. However it’s my belief that I would need a much larger battery than I have now (4 kWh) in order to benefit from this as it will always be generally better value to use that stored energy to avoid the early evening peak price period (up to 35 p/kWh) than to sell it back to the grid at a lower price and then need to buy more energy myself. If I had a bigger battery (both in terms of energy and power) then I could both meet my own needs and sell back to the grid.

Overall however I think that it’s clear that, with my current relatively small battery and deemed export tariff, I’m better off on the older FiT scheme than the newer SEG scheme even with one of the better-paying SEG providers.

I’ve been seeing a few online advertisements recently touting 70% savings on electricity through a combination of solar panels and battery storage. I’ve also been looking for a way to express my savings through my smart tariff so this seemed like a opportunity to try that.

My start point is a years data from my monitoring system..

Monitoring data for March 2019 to February 2020

I also went through a year of electricity bills (with slightly different start and end dates) concluding that my average purchased electricity cost was 7.08 p/kWh. Thus my average electricity costs (including solar) are on the right of the table below:

source

quantity

est unit price

Est total

my uniT price

mY total

my saving v. Est

Bought

4,309 kWh

15.75 p/kWh

£678.67

7.08 p/kWh

£305.08

£373.59

Generated

2,473 kWh

15.75 pkWh

£389.50

0.00 p/kWh

£0.00

£389.50

Total / Average

6,783 kWh

15.75 p/kWh

£1,068.32

4.5 p/kWh

£305.23

£763.09

Comparison between my electricity cost and the UK average

If I look at the Energy Saving Trust’s assumptions as a baseline, they have the average UK cost of electricity as 15.75 p/kWh. If I’m paying an average 4.5 p/kWh for each kWh used with my combination of solar PV, storage battery and smart tariff then I’m paying 28.6% of the cost of someone who used the same amount of electricity bought at the average UK rate or saving 71.4% of electricity cost. To put it another way, I’m paying £305.23 for electricity that would have cost the average UK consumer £1,068.32 (on the left of the table above) – a saving of £763.09.

(The baseline assumption that someone would have used the same amount of electricity as me without my level of technology is a slight over-estimate as I flex water heating between gas and electricity since my bought electricity price is sometimes lower than my bought gas price causing me to substitute electricity for gas. Someone on a conventional electricity tariff and gas would never make that substitution as their gas would always be cheaper than their electricity, hence my electricity consumption is a little higher than someone who would be on a conventional electricity tariff.)

I’m also generating feed-in tariff due to the age of my system (approximately 4.5 years old) which would be £714.59 per annum at current rates, and making 1,075 kWh of hot water from surplus solar electricity which saves £38.22 in gas (the diverted / hot water saving in the screenshot above is based on a notional electricity price, not a gas price). Unless I’ve missed something that’s an annual return of £1,515.90 (£763.09 + £714.59 + £38.22).

In my previous post I estimated my investment at £8,670 so with an combined annual savings and revenue of £1,515.90 that’s a 17.5% return or a payback of 5.7 years. Previously I’d estimated 9 years including the battery, but this was without the benefit of the smart tariff. As we’ve now had the solar PV for 4.5 years that’s very promising, although as my return seems to be accelerating it will take more than 4.5 past years + 1.2 future years (total 5.7 years) to achieve payback.

The current 5.7 years to payback would have achieved payback in spring 2021 as the near bookend, while the prior 9 years would have been autumn 2024 as the far bookend. In practice I could not have achieved the lower bookend of 5.7 years, even had I invested in all the supporting technologies simultaneously, because I’m combining the legacy Feed-in Tariff (FiT) scheme for my solar PV with the Octopus Agile dynamic smart electricity tariff which started in February 2018,

Discussion elsewhere prompted me to look into what I spent on what you might term my energy smart systems relating to electricity consumption, so I thought I’d document it here.

Item

Description

Cost

Comment

1

Solar photovoltaic system (4kW)

£5,500

Bundled with ImmerSUN.

2

Powervault battery storage (4kWh)

£2,000

Free installation as part of UKPN trial.

3

ImmerSUN management system with monitoring.

£600

Estimate based on today’s pricing.

4

Remote-controlled car charger.

£300

Modified used charger from eBay. My own software.

5

Raspberry Pi items to make HEMS

£200

My own software.

6

Wet goods automation (WIFIPLUG x 2)

£70

TOTAL

£8,670

Prior analysis of items #1-#4 in pre-Agile days has suggested a total of 9 years to achieve payback on this investment through use of around 85% of the generated energy. Solar panels are potentially good for over 20 years operation, although I doubt the lead-acid batteries will still be operating for anything like that long.

The combination of item #5 with my Octopus Agile dynamic smart electricity tariff has resulted in my average bought electricity price being 7.75 p/kWh in 2019, about half the UK average. I suppose that I could make the same judgements and program items manually each day, but the HEMS significantly reduces my time commitment to achieve that.

Item #6 is my most recent addition. The sophistication of the algorithm combining the Agile tariff with a simple model of the cycle of each device is such that I would never achieve such a high quality result manually. However the saving is perhaps only a three pence each day so maybe £10 per year on my Agile tariff and thus 7 years to pay for the two smart plugs.

Much of this content is thus around 7 years to payback. The HEMS is potentially much quicker, but relies on having smart systems to control such as battery storage and car charger.